Written by Regulation Asia.
Data has never been more essential to making informed decisions, particularly in private markets where internal data at financial institutions is typically thin.
Data is vital for financial institutions to make well-informed decisions as well as to meet regulatory requirements, particularly for internal credit risk modelling and benchmarking. Complete data is highly valuable, but in areas such as private credit, there are challenges to finding the sources that can help to model risk.
Amid the ongoing economic uncertainty brought on by the Covid-19 health crisis, regulators have been responding by encouraging (in some cases mandating) increasing lending to households and small businesses to help them stay afloat. As a result, the need for accurate credit risk assessment has become increasingly important – and this relies on data.
Credit risk data directly feeds into bank models, enabling them to assess if, and when, they may fall short of regulatory capital and liquidity requirements. The data consumed by the internal models also informs business and strategic decisions – which are increasingly important during times of uncertainty.
In private credit markets, credit risk data is often unavailable or incomplete within banks’ internal data sets. Where the data is available, banks are often hindered by its poor quality and barriers to accessing and using the data across business units. At the same time, there are inherent difficulties in sourcing better data from external parties due to a lack of data sharing between institutions and privacy issues, among other hurdles.
There is a clear industry-wide need for quality data sources that can help round out financial institutions’ existing internal data sets – particularly in private credit markets.
Addressing data gaps
To address this lack of high quality and complete data, Moody’s Analytics has led an initiative to create the world’s largest collection of private credit risk data, through collaboration with leading financial institutions.
The initiative – known as Data Alliance – allows lenders to augment internal datasets, discover gaps in data quality, and compare portfolio risk and lending practices against peers to generate actionable insights for their portfolios in private credit markets.
In addition to Moody’s Analytics broad asset class coverage, the Data Alliance database incorporates contributed data from over 125 member financial institutions across 25 markets – including data on commercial and industrial real estate, project finance, asset finance, and agriculture, with specialised consortia data on risk weighted assets, economic capital and profitability.
The Data Alliance data sets have the depth and granularity to support advanced analytics, collectively offering more than 100 million financial statements representing 20 million private firms globally, including data on over 2.9 million defaults. The contributions from member institutions encompass private firm data, and facility- and obligor-level default data based on Basel definitions.
The data is cleaned, standardised, anonymised and validated against 200 business rules, and in exchange for sharing this data, Data Alliance member institutions receive data validation and qualitative assessments, industry benchmark analytics and consensus estimates, data pools, customised model validation and calibration services. Members can additionally use the pooled data, models and a suite of data visualisation dashboards for custom analytics.
Data quality improves
The unique, collaborative approach ensures that data quality improves over time, as members benchmark their portfolios against peers, across multiple dimensions such as industry, size, and region, and contribute new data from this benchmarking process.
Ultimately, Data Alliance allows members to gain insight into normally opaque private credit markets, where internal data is often thin. Equally important, Moody’s Analytics leverages the anonymised data sets to calibrate, regularly validate, and develop industry-standard credit risk models and scorecards to address regulatory reporting needs and inform loan origination, portfolio, and monitoring practices.
Moody’s Analytics also provides off-the-shelf tools for visualising, querying, and benchmarking the data, including solutions for expected credit loss (ECL) and impairment analysis required under IFRS 9 and CECL; enterprise-wide stress testing; and probability of default and loan origination models.
During the current economic environment, such tools – and the data required for their effectiveness – can ensure that financial institutions are able to assess their loan portfolios for outsized risks, enhancing not only internal decision-making, but their ability to provide regulators with the necessary insights to inform policy-making efforts.
Alongside Data Alliance, the Orbis database operated by Bureau van Dijk, a Moody’s Analytics company, offers additional data on more than 365 million companies globally – from financial data and M&A deal information, to information on corporate ownership structures and adverse media.
Orbis data helps financial institutions address other sources of risk besides credit risk. With more than one billion ownership links and global ultimate beneficial owner information, the database allows for accurate screening for politically exposed persons (PEPs), sanctioned entities, and their connected parties.
Gaining access to better sources of unbiased, standardised data is not merely a question of credit cost and risk management; rather, it represents a strategic step to enhance risk functions and drive better outcomes for firms more broadly.
The quality and depth of data available is more crucial now than ever before, as banks work to mitigate heightened risk, volatility and uncertainty in financial markets as a result of Covid-19.
More information on Data Alliance is available here.
More information on Orbis is available here.